MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints

2025-11-21

AI Summary

Real-time robot obstacle avoidance in dynamic environments remains challenging, as conventional methods rely on static assumptions while learning-based approaches are limited by single-frame perception. Method: This paper proposes an active end-to-end navigation framework that introduces, for the first time, a multi-frame point constraint mechanism. A depth prediction network generates future-frame visual observations, and a moving-obstacle trajectory prediction module explicitly models spatiotemporal point constraints to enable proactive path planning. The framework tightly integrates multi-frame visual information with motion modeling within an end-to-end policy learning architecture. Contribution/Results: Extensive experiments in both simulated and real-world dynamic scenarios demonstrate significant improvements in obstacle avoidance success rate, navigation robustness, and safety. The approach effectively overcomes the dual limitations of static environmental assumptions and single-frame perception, establishing a new state of the art in reactive navigation under motion uncertainty.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Obstacle avoidance in complex and dynamic environments is a critical challenge for real-time robot navigation. Model-based and learning-based methods often fail in highly dynamic scenarios because traditional methods assume a static environment and cannot adapt to real-time changes, while learning-based methods rely on single-frame observations for motion constraint estimation, limiting their adaptability. To overcome these limitations, this paper proposes a novel framework that leverages multi-frame point constraints, including current and future frames predicted by a dedicated module, to enable proactive end-to-end navigation. By incorporating a prediction module that forecasts the future path of moving obstacles based on multi-frame observations, our method allows the robot to proactively anticipate and avoid potential dangers. This proactive planning capability significantly enhances navigation robustness and efficiency in unknown dynamic environments. Simulations and real-world experiments validate the effectiveness of our approach.

Problem

• Proactive navigation in dynamic environments via multi-frame constraints
• Overcoming static assumptions and single-frame limitations in robotics
• Enhancing obstacle avoidance through future path prediction

Innovation

• Multi-frame point constraints enable proactive navigation
• Prediction module forecasts future obstacle trajectories
• Direct integration of current and predicted frames